Diagnosis and Physical Modeling of Faults in a Three-Phase Induction Motor

Abstract

This thesis develops a simulation-based diagnostic study for a three-phase squirrelcage induction motor, supported by experimental parameter identification on a Siemens 1LA7096-4AA10 1.5 kW laboratory machine. A documented 500 W benchmark motor is first used to establish the power-invariant synchronous-𝑑𝑞 formulation, the inductance reconstruction procedure, and the analytical-versus-Simscape validation workflow. The Siemens motor is then identified from a phase-to-neutral stator-resistance measurement, no-load, blocked-rotor, load, and coast-down tests; its inertia is taken from the model-specific Siemens datasheet, while the viscousfriction coefficient is derived from the measured decay ratio. Using these identified Siemens parameters, a healthy 1.5 kW analytical model is validated against an independent Simscape implementation, a measured 8.5 Nm torque-speed operating point, and manufacturer rated data. The validated Siemens model is then used to generate and interpret deterministic simulation datasets for supply-voltage unbalance, incipient inter-turn short circuit, a BPFO bearing-torque surrogate, and broken-rotor-bar rotor asymmetry. The results show that several diagnostic indicators are strongly load-dependent: negative-sequence current factors, 100 Hz torque components, BPFO sideband indices, and BRB slip sidebands cannot be interpreted through fixed thresholds without load context. The work is therefore positioned as a physically grounded modeling and dataset-construction stage, not as a hardware fault campaign or a deployed classifier.